The Plant Cell, Vol. 27: 1018–1033, April 2015, www.plantcell.org ã 2015 American Society of Plant Biologists. All rights reserved.
Digital Single-Cell Analysis of Plant Organ Development
Using 3DCellAtlas
OPEN
Thomas D. Montenegro-Johnson,a,1 Petra Stamm,b,2 Soeren Strauss,c,2 Alexander T. Topham,b Michail Tsagris,d
Andrew T.A. Wood,d Richard S. Smith,c and George W. Basselb,3
a School
of Mathematics, University of Birmingham, Birmingham B15 2TT, United Kingdom
of Biosciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
c Max Planck Institute for Plant Breeding Research, 50829 Cologne, Germany
d University of Nottingham, Division of Statistics, School of Mathematical Sciences, Nottingham NG7 2RD, United Kingdom
b School
Diverse molecular networks underlying plant growth and development are rapidly being uncovered. Integrating these data into
the spatial and temporal context of dynamic organ growth remains a technical challenge. We developed 3DCellAtlas, an
integrative computational pipeline that semiautomatically identifies cell types and quantifies both 3D cellular anisotropy and
reporter abundance at single-cell resolution across whole plant organs. Cell identification is no less than 97.8% accurate and
does not require transgenic lineage markers or reference atlases. Cell positions within organs are defined using an internal
indexing system generating cellular level organ atlases where data from multiple samples can be integrated. Using this approach,
we quantified the organ-wide cell-type-specific 3D cellular anisotropy driving Arabidopsis thaliana hypocotyl elongation. The
impact ethylene has on hypocotyl 3D cell anisotropy identified the preferential growth of endodermis in response to this
hormone. The spatiotemporal dynamics of the endogenous DELLA protein RGA, expansin gene EXPA3, and cell expansion was
quantified within distinct cell types of Arabidopsis roots. A significant regulatory relationship between RGA, EXPA3, and growth
was present in the epidermis and endodermis. The use of single-cell analyses of plant development enables the dynamics of
diverse regulatory networks to be integrated with 3D organ growth.
INTRODUCTION
Advances in confocal imaging techniques have enabled the acquisition of 3D (Moreno et al., 2006; Truernit et al., 2008; Bassel
et al., 2014; Yoshida et al., 2014) and 4D data sets (Fernandez
et al., 2010; Maizel et al., 2011; Sena et al., 2011), such that the
extent and dynamics of cellular structures in plants can be captured. Key advantages to higher dimension imaging include the
more accurate and complete representation of the biological
system being analyzed. This is required for the inclusive understanding of plant growth and can provide insight not possible
using 2D imaging (Fernandez et al., 2010; Bassel et al., 2014;
Yoshida et al., 2014).
The comprehensive quantification of cellular growth is a central
objective in the analysis of image data sets, and procedures capable of quantifying cell elongation using 2D images have been
developed (Federici et al., 2012; French et al., 2012). These approaches are limited to the select number of cells being imaged
and are susceptible to measurement errors due to oblique optical
1 Current
address: Department of Applied Mathematics and Theoretical
Physics, Centre for Mathematical Sciences, University of Cambridge,
Wilberforce Road, Cambridge CB3 0WA, UK.
2 These authors contributed equally to this work.
3 Address correspondence to [email protected].
The author responsible for distribution of materials integral to the findings
presented in this article in accordance with the policy described in
the Instructions for Authors (www.plantcell.org) is: George W. Bassel
([email protected]).
OPEN
Articles can be viewed online without a subscription.
www.plantcell.org/cgi/doi/10.1105/tpc.15.00175
sectioning. A full 3D representation of cellular structure is required
both for accurate quantitative measurement and comprehensive
representation (Fernandez et al., 2010).
The ability to identify unique cell types from 3D images creates the possibility to perform virtual cell-type-specific analyses.
This can be accomplished through the introduction of distinct
cell lineage markers (Long et al., 2009; Federici et al., 2012) or the
creation of user-defined cellular reference lookup (Busch et al.,
2012; Schmidt et al., 2014). The ability to extend these approaches
to different organs and contexts relies upon additional user input
to define either novel reference atlases or to introduce lineage
markers into additional genetic backgrounds. Neither of these
approaches provides complete cell shape information.
The molecular networks underlying plant growth and development are being uncovered by the efforts of the research community through large-scale biological interaction network data at
multiple scales (Schauer and Fernie, 2006; Brady et al., 2007;
Braun et al., 2011; Bassel et al., 2012; Rhee and Mutwil, 2014).
However, the ability to quantify the dynamic spatial and temporal relationships between these components and their interactions within the context of multicellular organ development
remains limited.
In order to investigate whole-organ growth and integrate regulatory networks at single-cell resolution, we developed 3DCellAtlas.
This rapid and modular computational approach to generate
quantitative 3D cellular atlases of plant organ growth simultaneously identifies cell types and quantifies 3D cellular anisotropy
using the intrinsic geometric properties of distinct cell types. This is
achieved with greater accuracy than other published approaches
and does not require user-defined reference atlases or cell lineage
Quantitative 3D Cellular Organ Atlases
markers. This approach may be applied directly to diverse radially
symmetric plant organs. The ability to quantify reporter abundance
within individual cells in 3D facilitates cell-type and positionspecific analyses to establish the relationship between regulatory
network components and organ growth at a cell-type-specific
resolution. The implementation of 3DCellAtlas within the existing
publicly available software MorphoGraphX (www.MorphoGraphX.
org) makes this procedure easily accessible and allows quantitative outputs to be rapidly visualized in situ within the context of
the cellular structure of 3D organs.
The development of this methodology, which we used in the
analysis of Arabidopsis thaliana, enables whole 3D organ growth
patterns at single-cell resolution to be investigated, and it allows
the simultaneous integration of regulatory components within
these individual cells.
RESULTS AND DISCUSSION
Primary Image Acquisition and 3D Segmentation
Cleared samples were imaged using confocal microscopy as
previously described (Truernit et al., 2008; Bassel et al., 2014) and
z-stacks opened within MorphoGraphX (www.MorphoGraphX.
org) (Kierzkowski et al., 2012) (Figures 1 and 2A). A curved line,
termed a Bezier line, was placed through the geometric center of
the organ (Figure 1B; Supplemental Movie 1), and cells were segmented using ITK Autoseeded Watershed, an image processing
algorithm (Figure 2C) (Supplemental Figure 1) (Bassel et al., 2014;
Yoshida et al., 2014).
Local Axis Alignment
The cells of many radially symmetric plant organs have an approximately cuboid geometry (Bassel et al., 2014), such that three
Figure 1. Workflow for the Generation of Quantitative 3D Cellular Atlases.
1019
principle axes describe each of their three dimensions of anisotropic growth. 3DCellAtlas uses a combination of a central Bezier
curve and a mesh defining the surface of the organ to align these
local axes within the natural coordinate system for the organ
(Figures 2B and 2E; Supplemental Figure 2) (Hejnowicz, 1984).
The use of a Bezier curve better describes the contours of plant
organs than a straight line (Bassel et al., 2014) owing to the
curved nature of their growth habit.
Meshes describing cell surfaces were generated by segmenting
the stack in 3D using the ITK Morphological watershed filter and
then using the marching cubes algorithm (Lorensen and Cline,
1987). An additional mesh describing the overall surface of the
organ was also extracted (Figures 2D and 2E; Supplemental Figure
1). Cell dimensions were determined using the directions defined
by the coordinate system, with the three principal directions of
each cell, representing the longitudinal, radial, and circumferential
axes, measured from the centroid position within each individual
cell (Figures 2F to 2H; see Methods). The length of each of these
axes represents the simplified 3D dimensions of each cell.
Non-strictly symmetric features, for example, the circumferential flaring of the Arabidopsis embryo (Supplemental Figure 3) or
tapering of roots and embryos (Supplemental Figure 4), were incorporated through linear interpolation of the Bezier and surface
axes (Methods), enhancing the accuracy of the axis alignment.
Geometry-Based Identification of Cell Types
Different cell types in plant organs frequently have unique
shapes, an intrinsic property that this method uses to differentiate them without additional information. For example, the hypocotyl of the mature Arabidopsis embryo shows cell-type
specificity with respect to circumferential cell lengths, with
greater lengths observed in the cortical cells relative to the
epidermis and endodermis (Figure 2I).
1020
The Plant Cell
Figure 2. Label-Free Geometry-Based Identification of Cell Types from 3D Images.
(A) Embryonic Arabidopsis axis z-stack maximum intensity project.
Quantitative 3D Cellular Organ Atlases
By plotting a distinguishing linear dimension of cell geometry on
one axis, and the scaled radial distance of the cell from the central
Bezier on the other axis, clusters of distinct cell types are produced on 2D heat map plots (Figure 2J; Supplemental Figure 5).
Selection of these cell-type-specific clusters within a graphical
user interface (GUI) implemented in 3DCellAtlas enables userdefined annotations of these clusters to demarcate distinct cell
types. Within the GUI, clusters in the 2D heat map are seeded
with clicks and annotated by the user with numbers. These
numbers represent annotation labels for selected cell types and
are stored within the “parents” variable for each segmented cell in
MorphoGraphX. Diverse organs may have different geometric
features in their cells that may represent unique geometric
properties of a given cell type that can be used to uniquely identify
it. Users may select any of the three principal directions to generate 2D heat map plots and generate distinct clusters of cell
types (Supplemental Figure 5).
Following the user-defined assignment of unique cell-type
numbers to clusters in the 2D GUI heat map, the cells of 3D
segmented organs in the active window are immediately false
colored using distinct colors for each assigned parent label or cell
type. By painting unique selected cell types with different colors,
users can evaluate the quality of the cluster selection that has
been performed. If there are inaccuracies, the user can return to
the cell cluster selection GUI to iteratively adjust the locations of
the seeds and enhance the accuracy with which distinct cell types
are identified. This real-time iterative adjustment of cluster seeding provides users the opportunity to rapidly increase cellular
annotation accuracy. Cells that are not segmented properly represent inaccurate geometric representations and cannot be accurately identified owing to their geometric irregularities. They
should as well be disregarded from future statistical analyses for
the sake of their geometric inaccuracies.
In addition to the segmentation of cells, air spaces between
cells (Cloetens et al., 2006) are also captured. These segments
do not represent genuine cellular data points, but nonetheless
require accurate identification and annotation to facilitate their
1021
removal to avoid confounding further analyses. Air spaces typically have shorter dimensions and are generally located
between adjacent cell layers within the 2D heat map plots displaying cell geometries. These can be selected within the GUI and
assigned a unique parent label enabling their removal from future
analyses (Figure 2K; Supplemental Figure 5).
This approach works on diverse radially symmetric organs from
Arabidopsis, including hypocotyls (Figures 2L and 2M), roots
(Figures 2N to 2P), and mature embryo axes (Figures 2Q to 2S).
While these examples are provided using Arabidopsis organs, this
method is applicable to any radially symmetric organ, for example, a poppy (Papaver rhoeas) hypocotyl (Figures 2T and 2U).
Assignment of the Columella
Up to this stage, the annotation of cells in organs started from
the first cortical cell onwards, leaving the columella in roots and
embryo radicles unannotated (Figures 2N and 2R). To complete
the assignment of an identity to these cells, a single click of the
“assign columella” annotates the unassigned cells (Figures 2O
and 2S). The columella is annotated as those cells not already
having been assigned a label, and the surrounding lateral root
cap identified as separate from the columella based on these
cells having a deficiency in shared surface area with their adjacent cells (see Methods).
Topological Identification of Misannotated Cells
Radially symmetric plant organs by definition consist of concentric rings of cells. The physical associations between these
adjacent cell types are therefore conserved through organs
having this topology. We used this feature to automatically
identify cells which were misannotated using the initial geometrybased cell identification.
To achieve this, physical cellular interaction networks between cells from whole segmented organs were generated by
identifying shared triangles between the surface meshes of
Figure 2. (continued).
(B) Alignment of a Bezier curve (white line) through the axis in (A).
(C) 3D segmented axis in (A) with different colors indicating unique segments.
(D) The mesh describing the surface of the organ is represented in white.
(E) A section of the hypocotyl of the embryo in (A) where the cells are segmented (in blue) and the white line indicates the location of the Bezier curve.
The surface mesh is shown in light red surrounding the organ axis. Bar = 30 mm.
(F) Hypocotyl cross section showing alignment of axes in 2D and a single cell in 3D. The location of the Bezier is represented by a pink dot.
(G) and (H) Aligned local axes within an individual cortical cell (G) and across the epidermal cells (H) of the hypocotyl from (E).
(I) Cells false colored by their circumferential length in the hypocotyl section in (E) with the scale in microns.
(J) Clustering of cell types based on their circumferential length and scaled radial distance from the Bezier.
(K) Annotation of the cell clusters in (J).
(L) and (M) Identified cell types in the Arabidopsis hypocotyl given unique colors in longitudinal and cross section. Bars = 50 mm.
(N) and (O) Identified cell types in the Arabidopsis root in a virtual longitudinal section before (N) and after (O) the assignment of the columella. Bar = 20 mm.
(P) Mature root in a virtual cross section. Bar = 20 mm.
(Q) to (S) Identified cell types in the Arabidopsis embryo axis.
(Q) Virtual cross section. Bar = 10 mm.
(R) and (S) Longitudinal section before the assignment of the columella (R) and after the assignment of the columella (S). Bar = 20 mm.
(T) Longitudinal section of a poppy hypocotyl. Bar = 50 mm.
(U) Cross section of a poppy hypocotyl. Bar = 50 mm.
1022
The Plant Cell
adjacent cells (Figures 3A to 3C; Supplemental Figure 6; see
Methods) (Yoshida et al., 2014). Cellular connectivity graphs were
annotated by cell type using the geometry-based cell identification method described above. Topological validation using the
cellular interaction network data was performed to identify misannotated cells (Long et al., 2008). Using the process “topological
check,” the frequencies of edges between cell types were established, and low-frequency or “impossible” edges in the graph
were identified (Figure 3D). These edges highlight the presence of
a misannotated cell that can then be identified and are selected as
part of the process (Figures 3E and 3F). This method is effective at
highlighting the presence of misannotated cells, while cells adjacent to errors can also become highlighted given their association to incorrect cells (Figure 3F). The manual correction of these
annotation errors facilitates the correct labeling of all cells within
segmented organs in the minority of instances where the semiautomated method fails.
An automatic method to correct cell annotation errors was also
developed. A common error is the misannotation of endodermis as
vasculature given the close proximity of these two cell types in the
2D heat maps. Using the “examine vasculature” process, endodermal cells that have been incorrectly annotated are automatically
identified based on their similar distance and geometry to correctly
annotated endodermal cells, as identified using topological validation. The labels of these cells are adjusted accordingly, which
increases the accuracy of cellular annotations for this cell type
(Figure 3G).
This annotation method combined with topological correction
has a minimum of 98.2% accuracy for Arabidopsis hypocotyls,
mature embryos, and roots (Table 1) and was accurate to a minimum of 97.8% in poppy hypocotyls.
A Cortical Reference System for Comparative Analyses
In order to assign individual cells within organs to an indexed
system, an internal cellular referencing system was established.
This enables direct comparison of organ properties between different organs at different stages of development at the resolution
of individual cells. The cortical cells of roots, hypocotyls, and
embryos show the greatest geometric uniformity (Bassel et al.,
2014) and are relatively evenly distributed across the circumferential arrangement of plant organs. Cortical cells were therefore
chosen to establish an intrinsic positional referencing system.
To establish the cellular referencing system, the first cortical
cell in a sample is selected by the user (Figure 4A), which assigns this cell a position number of 1. The “assign cortical cells”
process uses this reference point to index all cells in the organ.
Sequential cells following the same linear file of cortical cells
along the axis are assigned incremental position numbers, and
these positional values were projected radially across the organ.
All other cells within the same longitudinal position are given the
same cell number position, along with other cell types that lay
within the window of this cell position (Figure 4B). In this way, all
cells within the organ are assigned a cell type and a cell position.
These quantitative values can be visualized by false coloring
axes within the “display cell data” process of the 3DCellAtlas
plug-in and selecting “associated cortical cell.” This process
also enables the volume (Figure 4C), surface area (Figure 4D),
longitudinal cell length (Figure 4E), radial cell length (Figure 4F),
and circumferential cell length (Figure 4G) of individual cells to
be visualized in situ through the false coloring of segmented
organs.
Quantitative data from 3DCellAtlas can be saved and loaded
from within the plug-in using the “save cell data” and “load cell
data” processes, respectively. Saved text files contain cell
geometric data including cell volume, surface area, longitudinal
length, radial length, and circumferential length, the cell position
within the internal referencing system, and the parent labels that
define cell identity. The locations where seeds were placed within
2D heat map GUIs to select cell type clusters are also saved and
can be loaded from saved files, which allows modifications to
a previous work flow to be made. Exported text files from multiple
samples or time points enable the statistical analysis of these
quantitative cellular geometric data.
Single-Cell 3D Phenotyping of Hypocotyl Growth
Studies have previously examined the elongation of epidermal
cells during hypocotyl growth in Arabidopsis (Gendreau et al.,
1997), yet the growth of cells within this multicellular organ and
their 3D anisotropy have yet to be examined. We sought to
quantify the organ-wide cell-type-specific 3D anisotropy driving
seedling establishment in the Arabidopsis hypocotyl using the
cellular geometric data generated by 3DCellAtlas. The growth of
the hypocotyl up to 4 d of development does not involve significant cell divisions (Gendreau et al., 1997), enabling multiple
fixed samples at different stages to be used in statistical analyses of 3D growth. The hypocotyl within the mature embryo axis
was used as the baseline unexpanded state, and cell geometries
were compared with seedlings grown for 4 d.
The use of the cortical referencing system defined cell positions
within samples, enabling equivalent individual cells from different
samples to be quantitatively compared. A total of four samples for
each the unexpanded (embryonic hypocotyl) and expanded
states (4 d hypocotyl) were collected. Statistical analyses were
performed on these pooled data (see Methods), whereby output
files from these multiple samples were compiled into a single
directory and analyzed at cell-type and position-specific resolution using the “3D cell growth analysis” process (Figure 5). Outputs of the 3D cell growth analysis process include text files that
can be used to plot the mean and confidence intervals for data on
a cell-type and position-specific basis (Supplemental Figure 7).
Importing these text files as a heat map in MorphoGraphX allows
for the false coloring of a 3D segmented organ mesh and the in
situ visualization of differences in cell size within the cellular
context of the organ. The data presented in Figure 5 represent the
false-colored means of these analyses.
During the elongation of hypocotyls in the light, relative volumetric cell expansion is greatest in the inner cell layers and
decreases progressively toward outer cell layers (Figure 5A). A
gradient of expansion of high to low growth was observed from
the base of the hypocotyl moving upwards in the epidermis and
outer cortical cells. A similar gradient of increase in relative
surface area was observed in both cortical cell layers and to
a lesser extent in the epidermis (Figure 5B). The endodermis
underwent the greatest increase in surface area growth.
Quantitative 3D Cellular Organ Atlases
1023
Figure 3. Topological Identification and Correction of Misannotated Cells.
(A) Surface rendering of radial section of segmented embryonic hypocotyl cells. Bar = 10 mm.
(B) Close-up of (A) showing the mesh defining the cell surfaces.
(C) Cellular connectivity network depicting the cells in (A) and their associations in 2D. Distinct cell types in the network are given unique colors.
(D) Schematic cellular connectivity network illustrating the identification of impossible edges. Unique cell types are illustrated using the same color
scheme for cell types used in (C). A misannotated cell is identified based on the “impossible” association between the orange endodermal cell and the
cyan outer cortical cell. Impossible edges are colored red in this schematic.
(E) Embryonic Arabidopsis hypocotyl following the assignment of cell types.
(F) Output of the topological check identifying misannotated cells.
(G) Correction of misannotated endodermal cells by the “check vasculature” process.
1024
The Plant Cell
Table 1. Accuracy of Geometry-Based Cell-Type Identification within 3D Images of Radially Symmetric Plant Organs
Epidermis
Arabidopsis root
Arabidopsis
hypocotyl
Arabidopsis
mature embryo
axis
Poppy hypocotyl
Outer Cortex
Inner Cortex
Endodermis
No. of Cells
Analyzed
Accuracy
Accuracy
before
No. of Cells “Examine
Analyzed
Vasculature”
Endodermis after
“Examine
Vasculature”
No. of Cells
Analyzed
Accuracy
No. of Cells
Analyzed
Accuracy
399 6 22
291 6 65
98.9 6 0.2
99.5 6 0.9*
180 6 11
138 6 28
98.4 6 0.2 N/A
98.9 6 0.5 74 6 25
N/A
100 6 0
213 6 15
86 6 25
90.5 6 0.7
97.7 6 4.0
98.2 6 0.11
99.4 6 1.0
837 6 43
99.7 6 0.1
394 6 21
99.4 6 0.7 203 6 25
99.3 6 0.2 239 6 48
92.9 6 2.3
98.7 6 0.8
584 6 230
99.9 6 0.1
412 6 18
99.1 6 0.8 228 6 30
98.7 6 1.9 179 6 28
90.8 6 4.9
97.8 6 3.4
The number of cells of each cell type present in the organs analyzed is indicated along with the standard deviation across the three samples analyzed. Accuracy
is represented as the percentage of cells whose identity was correctly identified from the total number of cells for a given cell type in a given organ. The variation
is the standard deviation of the percentage accuracy for each of the three samples per organ analyzed. The percentage accuracy both before and after running
the “examine vasculature” process in the endodermis is indicated in the final two columns. A total of three samples for each organ was analyzed to generate
these accuracy count data. Asterisk indicates that the upper limit of the variation in accuracy counts was truncated at 100%. N/A, not applicable.
Relative longitudinal cell expansion was greatest at the base of
the hypocotyl as previously reported (Gendreau et al., 1997) and
evenly distributed across cell types (Figure 5C). Radial cell elongation was greatest in the cortical cells of light-grown hypocotyls
and significantly less in the epidermis and endodermis (Figure
5D). Conversely, circumferential growth was greatest in the epidermis and endodermis and much less in the cortex (Figure 5E). A
gradient of increasing circumferential growth was observed in the
endodermis and epidermis moving up the hypocotyl.
The calculation of organ-wide 3D cell anisotropy driving hypocotyl elongation in the light confirmed the previous observation that there is a gradient of decreasing cell elongation moving
up the hypocotyl (Gendreau et al., 1997). It has also provided
information as to the anisotropies of the epidermis and inner cell
types, including both cortical cell layers and endodermis during
hypocotyl growth. These data collectively demonstrate the cortex
to be growing more in the radial direction and possibly compressing the epidermis and endodermis, respectively.
Figure 4. Internal Reference System and Whole Organ Quantification of 3D Cell Geometry.
(A) Longitudinal section of a segmented mature Arabidopsis root with the first cortical cell selected (in red) used to align local cell position reference
system. Bar = 20 mm.
(B) False coloring of all cell types along the length of the root, which have been assigned an associated cortical cell position based on their longitudinal
position along the Bezier curve. The scale indicates cortical cell position, with cells in the columella assigned a position of 0.
(C) Cell volumes along the length of the root false colored using cubic microns on the scale.
(D) Cell surface area false colored on the axis using square microns on the scale.
(E) Cell length parallel the Bezier curve.
(F) Radial cell length emanating from the center of the organ.
(G) Cell circumferential length going around the radial orientation of the organ.
Quantitative 3D Cellular Organ Atlases
Figure 5. Single-Cell 3D Phenotyping at Cell-Type and Position-Specific Resolution in Arabidopsis Hypocotyls.
1025
1026
The Plant Cell
Ethylene Modulates Hypocotyl 3D Cellular Anisotropy
Visualizing 3D Cellular Anisotropy
A previous study reported that the treatment of light grown hypocotyls with the ethylene precursor 1-aminocyclopropane1-carboxylic acid (ACC) leads to increased epidermal cell elongation
in Arabidopsis hypocotyls (Smalle et al., 1997). To quantify the
influence of this hormone treatment on the organ-wide cell-typespecific 3D anisotropy of hypocotyl growth in the light, we subjected 4-d-old ACC-treated light grown hypocotyls to quantitative
cellular phenotyping using 3DCellAtlas (Figures 5F to 5J).
The maximum relative increase in cell volume doubled in
hypocotyls treated with ACC relative to control samples (Figures
5A and 5F; Supplemental Figure 8). This supports the finding
that ethylene inhibits the light-mediated limitation of cellular
expansion in Arabidopsis hypocotyls.
The pattern of 3D cell anisotropy in ACC-treated hypocotyls
also differed from light controls. To visualize these differences,
false-colored axes showing the ratio of ACC cell growth over
that in the wild type were generated (Figures 5K to 5O).
The overall expansion of the inner cell layers in ACC-treated
hypocotyls was greatest and decreased progressively toward
the outer cell layers (Figure 5K). Both cell expansion and cell
elongation (Figure 5M) were greater in the lower section of the
hypocotyl in ACC-treated samples than in light grown controls,
particularly in the endodermis. These data indicate ethylenemediated enhanced cell expansion in hypocotyls to be greatest
both in the inner cell layers and toward the base of this organ.
The radial expansion of endodermal cells was up to 50% greater
in ACC-treated samples and decreased progressively toward the
outer cell layers with the epidermis showing little difference with
control hypocotyls (Figures 5I and 5N). The circumferential growth
of the endodermis was very similar between treatment and control
samples, with the outer cortex showing the greatest increase
growth in this direction in ACC samples (Figure 5O).
Collectively, these data indicate the growth response of the
endodermis to be most affected by ACC treatment in light grown
hypocotyls. The observed anisotropic growth in this cell type
could either be due to enhanced ethylene response within this
cell type, an intrinsic anisotropy of this tissue, or to the loosening
of the neighboring cortical cell wall, leading to radial expansion.
Patterns of dominant cell anisotropy in 3D were quantified by
calculating the elongation factors (E-factors) of cell expansion in
each of the three principal directions. E-factors represent the ratio
of cell growth in a given direction divided by the sum of elongation in all three directions (Methods). In this way, the relative
elongation in a given direction relative to total elongation may be
determined.
E-factors for each light-grown and ACC-treated hypocotyl were
calculated using the “3D cell growth analysis” process. False coloring of the active 3D segmented sample in the MorphoGraphX
window with each of the three principal E-factor directions can be
achieved by importing the text files containing these data as a heat
map.
Anisotropy in the longitudinal direction was strikingly similar
between control and treated hypocotyls (Figures 5P and 5Q),
with only slightly less extension in the lower epidermis and outer
cortex in ACC-treated samples. This indicates that despite ACC
leading to larger and thicker hypocotyls, this treatment does not
influence longitudinal anisotropy relative to total cell elongation
along all three principal directions of growth.
Radial anisotropy was slightly greater in the lower cortical cells
of ACC-treated samples than corresponding controls and much
greater in the endodermal cells (Figures 5R and 5S). This calculation reflects the greater relative radial expansion of the endodermis in ACC-treated samples relative to untreated light grown
counterparts (Figures 5D and 5I).
The pattern of circumferential anisotropy between light-grown
and ACC-treated hypocotyls was similar with the exception of
greater growth in this direction within the endodermis of lightgrown samples (Figures 5T and 5U). This observation is also not
intuitive given the strong differences in the pattern of relative
circumferential extension between each light-grown and ACCtreated hypocotyls (Figures 5E and 5J).
These data collectively demonstrate the anisotropic growth of
the endodermis, and to a limited extent the epidermis, to be differentially regulated by the ethylene signaling pathway. ACC application
increases radial anisotropy of the endodermis while decreasing
circumferential elongation according to these observations. These
Figure 5. (continued).
(A) to (E) 3D quantification of a wild-type Arabidopsis hypocotyl grown in the light for 4 d with false coloring indicating mean relative changes in cell
volume (A), cell surface area (B), longitudinal length (C), radial length (D), and circumferential length (E). Scales represent the ratio of growth above the
embryo hypocotyl control.
(F) to (J) 3D quantification of a wild-type Arabidopsis hypocotyl grown in the light and in the presence of 30 mM ACC for 4 d with false coloring indicating
mean relative changes in cell volume (F), cell surface area (G), longitudinal length (H), radial length (I), and circumferential length (J). Scales represent
the ratio of growth above the embryo hypocotyl control.
Bars in (A) and (F) = 50 mm.
(K) Ratio of cell volume of ACC-treated hypocotyls over controls.
(L) Ratio of cell surface area of ACC-treated hypocotyls over controls.
(M) Ratio of longitudinal cell length of ACC-treated hypocotyls over controls.
(N) Ratio of radial cell length of ACC-treated hypocotyls over controls.
(O) Ratio of circumferential cell length of ACC-treated hypocotyls over controls.
(P) and (Q) Longitudinal E-factors in light-grown (P) and ACC-treated (Q) hypocotyls.
(R) and (S) Radial E-factors in light-grown (R) and ACC-treated (S) hypocotyls.
(T) and (U) Circumferential E-factors in light-grown (T) and ACC-treated (U) hypocotyls. Scales indicate absolute E-factor values.
Quantitative 3D Cellular Organ Atlases
analyses are aided by the visualization of organ-wide cell-typespecific cellular anisotropy using E-factors so that unintuitive
patterns of growth can be revealed.
Collectively, these analyses demonstrate the endodermis to
be a cellular site of preferential growth in response to ethylene in
Arabidopsis hypocotyls and specifically its anisotropic growth in
the radial and circumferential directions.
Quantification of Reporter Abundance within Individual Cells
and Relationship with 3D Growth
Multichannel confocal imaging enables both the cellular structure
and abundance of reporter constructs to be captured simultaneously. Following the 3D segmentation of plant cells, the total
abundance of signal within the defined boundaries of individual
cells can be quantified (Figures 6A to 6D) (Yoshida et al., 2011).
The quantification of reporter abundance within individual cells
together with cell position, identity, and 3D anisotropy enables
single-cell multidimensional imaging using 3DCellAtlas. Quantification of reporter abundance in 3D is more accurate than that in
2D, which may be subject to errors due to incomplete imaging of
cells and partial measurements depending on the plane that is
imaged.
Spatial Distribution of Endogenous DELLA Protein
Concentration during Root Growth
DELLA proteins are well characterized repressors of cell growth
(Harberd et al., 2009). The selective proteolysis of DELLA proteins
following the stimulus for a cell to grow, including the response to
the hormone gibberellic acid (GA), leads to the repression of this
repressor and a reversible growth switch (Harberd et al., 2009).
While the repressive function of DELLAs is well characterized on
a biochemical level, less is known about the spatial and temporal
regulation of these endogenous proteins in relation to organ
development.
We sought to explore the relationship between the cell-typespecific abundance of the DELLA protein RGA (REPRESSOR
OF ga1-3) and cell expansion patterns during Arabidopsis root
development to understand how the abundance of this growth
repressor relates to observed growth. We generated a RGA:RGAGUS (b-glucuronidase) translational fusion that could be quantified within the context of individual cells across growing roots.
The total reflectance of GUS crystals formed following GUS
staining (Truernit et al., 2008; Bassel et al., 2014) was used to
identify the relative concentration of reporter within individual cells
of the Arabidopsis root meristem.
The concentration (volume normalized abundance) of the endogenous RGA protein was false colored onto a 3D segmented
root (Figure 6E) to visualize its cell-type-specific distribution. The
relative abundance of the RGA protein reporter shows successive
peaks emanating from the tip of the root, with that in the epidermis being closest to the quiescent center, followed by an intermediately positioned peak in the cortex and a distal protein
abundance peak in the endodermis. These data were plotted
together with changes in cell volume for each cell type (Figures
6G to 6I). Reporter data were frequency normalized for comparative purposes in these graphs to explore the cell-type-specific
1027
relationships between these components. The average volumes
for each cell type at defined positions were calculated as described for the hypocotyl. Absolute volumetric cell expansion increased progressively along the length of the root tip, while the
observed decrease in endodermal cell volume can be accounted
for by the enhanced cell division rate in this cell type.
Peaks of RGA protein concentration do not relate to the
progressive expansion of cells along the length of the root longitudinal axis. The relationship between RGA protein concentration and cell volume across the different cell types of the root
was established statistically using linear regression (Table 2).
The regression analysis revealed no evidence of a significant
relationship (P value > 0.05) between RGA protein concentration
and change in cell volume for any of the cell types examined.
Integration of Endogenous DELLA Protein Abundance with
Downstream Expansin Expression and Cell Growth
One proposed mechanism by which DELLAs limit cell expansion
is through the inhibition of expansin gene expression (Cao et al.,
2006). Expansins are a well characterized class of cell wall
protein that promotes the loosening of cell walls and enable cell
expansion (Cosgrove, 2005). EXPANSIN-A3 (EXPA3) is expressed during Arabidopsis root development, and expression
of this gene is increased following GA application in the GAdeficient background ga1-5 (Goda et al., 2008), suggesting
transcription of this gene and activity of the upstream promoter
is regulated by DELLA proteins. Using the multidimensional imaging approach provided by 3DCellAtlas, we examined the spatiotemporal relationship between the abundance of the endogenous
RGA protein, activity of the EXPA3 promoter, and cell expansion in
a growing Arabidopsis root.
The activity of the EXPA3 promoter increases progressively
along the length of the root in the epidermis, cortex, and endodermis in a roughly similar pattern to cell volume increase (Figures
6F to 6I). Using linear regression across the different cell types for
each EXPA3 promoter activity and cell volume, a significant
positive relationship between the activity of this promoter and cell
expansion is observed in all cell types of the root (P value < 0.05)
(Table 2).
To determine whether a significant relationship between the
RGA protein and EXPA3 promoter activity is present, we performed a linear regression comparing the means of each of these
components at defined cell positions across distinct cell types.
The R2 values and P values from this analysis are given in Table 3.
There is evidence of a significant relationship for the epidermis
and endodermis (P = 0.014 and P = 0.044, respectively), but not in
the cortex. In both cases, the relevant regression coefficient was
negative, suggesting a weakly significant negative relationship
between RGA and EXPA3 as expected based on DELLA regulation of expansins.
These observations collectively suggest that the activity of the
EXPA3 promoter strongly correlates with cell size in all cell types of
the root examined. The regulation of the endogenous RGA protein
does not directly correlate with cell volume and only weakly relates
to EXPA3 promoter activity in the epidermis and endodermis. The
lack of a significant relationship between the RGA protein and cell
volume suggests that other factors downstream of RGA that
1028
The Plant Cell
Figure 6. Multidimensional Quantification of Cell Size, DELLA Protein Abundance, and Expansin Promoter Activity in the Mature Arabidopsis Root.
(A) Surface rendering of segmented embryonic cortical cells.
(B) 3D capture of GUS reporter activity by collection of reflectance of crystals generated following GUS assays using 5-bromo-4-chloro-3-indolyl-b-Dglucuronic acid as a substrate.
(C) Encapsulation of GUS activity within individual 3D segmented cells.
(D) False coloring of the total concentration of GUS reporter activity within individual cells in 3D in relative units.
(E) Concentration of the endogenous RGA protein within individual cells types at specific cell positions along the mature Arabidopsis root.
(F) Cell type and position specific activity of the EXPA3 promoter along the length of the mature Arabidopsis root. The scales in (E) and (F) are frequency
normalized to the total amount of reporter signal within each sample.
(G) Plot of average RGA protein abundance, EXPA3 promoter activity, and average cell volume in the root epidermis by cortical cell position.
(H) Same as (G) in root cortical cells.
(I) Same as (G) in the root endodermis. Reporter values in (G) to (I) are frequency normalized to illustrate relative abundances.
determine cell volume have not been observed or accounted for in
this analysis and that RGA regulates cell volume in the root
through more than just EXPA3. This observation fits with there
being many more genes that promote cell expansion being expressed in the root than just EXPA3 (Brady et al., 2007).
The ability to map single-cell data from diverse samples into
a common framework for comparative purposes using 3DCellAtlas
enables the quantitative relationships between various regulatory
components at cell-type-specific resolution to be established.
This represents a powerful approach to understand the relationship between gene regulatory networks and cellular growth
across whole organs. The data also provide a means to parameterize models to recapitulate these observed behaviors
(Band et al., 2012, 2014).
Quantitative 3D Cellular Organ Atlases
1029
Table 2. Output of Linear Regression between RGA Protein Concentration and EXPA3 Promoter Activity and Cell Volume across Different Cell Types
of the Expanding Arabidopsis Root
Coefficient
Cell Type
RGA and Cell
Vol.
Epidermis
20.00575
Cortex
0.00512
Endodermis
0.00487
t-Value
SE
EXPA3 and Cell RGA and Cell
Vol.
Vol.
EXPA3 and Cell RGA and Cell
Vol.
Vol.
0.10873
0.07099
0.14115
0.013
0.012
0.0164
0.023
0.012
0.018
20.242
0.426
0.278
P Value
EXPA3 and Cell RGA and Cell
Vol.
Vol.
EXPA3 and Cell
Vol.
8.482
5.714
8.614
3.71$10216
4.88$1028
2.77$10215
0.809
0.671
0.781
Roots were imaged and both cell geometric data and quantitative protein concentration used to fit linear models to explain these quantitative
relationships. A P value < 0.05 indicates a significant relationship.
While this example uses GUS reflectance, the quantification of
signal within individual cells may be performed using any reporter construct including fluorescence. The use of ratiometric
quantification of a reporter relative to an internal control using
existing software may facilitate the quantitative analysis of reporter abundance in individual cells (Federici et al., 2012; Pound
et al., 2012; Liao et al., 2015).
Quantitative 3D imaging of plant development is an increasingly
important approach in the study of plant developmental biology.
With the growing use of this approach comes an increased need
for computational tools capable of extracting biologically relevant
information from these data.
3DCellAtlas is a rapid and modular computational approach to
generate quantitative cellular level atlases of plant organ development. This pipeline enables the quantification of organ-wide
3D cell anisotropy driving organ growth at single-cell resolution.
Atlases generated also allow for multiple reporters and data types
to be quantified and mapped onto a common cellular level template. The quantitative and integrative analysis of diverse network
components can then be explored within the spatial and temporal
context of 3D cell shape changes across developing organs.
A key advance in computational methodology provided by
3DCellAtlas is the use of a warped 3D coordinate system (Figure
2H). This can accommodate the nonlinear growth of plant organs, and it enables the accurate alignment of axes representing
the principal directions of growth in radially symmetric organs.
These accurately aligned axes facilitate the rapid label-free
identification of cell types from 3D images without the need for
transgenic lineage markers or reference atlases, as well as the
complete quantification of 3D anisotropy across whole organs
and cell types.
Another key advance provided by 3DCellAtlas is the generation
of an internal cellular referencing system within whole-plant organs.
This enables data from diverse samples to be integrated onto
a common template at cell-type- and position-specific resolution.
These advances enable virtual single cell analyses to be performed across whole organs and the integration of diverse network components within the context of 3D cell shape changes.
The organ-wide cell-type-specific 3D cell anisotropy driving
Arabidopsis hypocotyl elongation in the light was quantified.
Differences in the radial cell anisotropy of the endodermis, relative to other cell types, was captured.
The influence of ethylene on 3D cell anisotropy in the hypocotyl was also quantified and led to the identification of the
endodermis as a cellular site of preferential growth in response
to ethylene application in hypocotyls. The role for GA signaling
in the endodermis regulating root elongation has been demonstrated previously (Ubeda-Tomás et al., 2008). The preferential
growth of the endodermis in response to ethylene suggests this
cell layer may facilitate hypocotyl growth in response to this
hormone. These analyses also revealed the presence of a gradient of increased cell growth toward the inner cell layers in the
hypocotyl in response to ethylene.
This approach is not limited by species, and the accurate
identification of cell types using the quantification of 3D cellular
geometric properties is possible in other species, including
P. rhoeas (Figures 2T and 2U).
The ability to investigate the quantitative spatiotemporal dynamics of multiple components of a regulatory network across
organ growth at cell-type-specific resolution was also demonstrated by examining the relationship between the DELLA protein RGA, the promoter activity of the expansin gene EXPA3,
and observed cellular growth in Arabidopsis roots. A strong
positive relationship between EXPA3 promoter activity and cell
expansion was observed in all cell types examined, while no
significant relationship between RGA protein abundance and
cell volume was observed. A marginally significant relationship
between the RGA protein and EXPA3 promoter activity was
identified in the epidermis and endodermis. These quantitative
relationships collectively demonstrate that EXPA3 expression is
closely related to cell expansion in roots. In addition, the RGA
protein regulates cell expansion through more than just EXPA3,
an observation consistent with the large number of cell-wallrelated genes whose expression is influenced by DELLA proteins (Cao et al., 2006).
Table 3. Output of the Linear Regression between RGA Protein
Abundance and EXPA3 Promoter Activity across Different Cell Types
of the Arabidopsis Root
Cell Type
R2
P Value
Epidermis
Cortex
Endodermis
0.512
0.114
0.381
0.014
0.457
0.044
R2 indicates the fraction of the variation in EXPA3 promoter activity
accounted for by RGA. A P value < 0.05 represents a significant
relationship.
1030
The Plant Cell
The quantitative description of this network integrated within
the context of 3D cellular growth across a whole plant organ was
facilitated by the methodology presented here. These results are
able to identify and quantitatively characterize the complexity of
the genetic regulation of growth in the root.
As an increasing number of reporters and key interactions
regulating plant development are generated and described, the
need to integrate this into the spatial and temporal context of
multicellular organ growth will increase. The robust nature of this
approach will facilitate these integrative multidimensional studies. The simultaneous identification of cell type, quantification of
3D cell shape changes, and reporter abundance within individual
cells allows the relationships between these variables to be
established and the modulation of cellular growth by regulatory
network activity to be defined at single-cell resolution. These
data may be used to parameterize models of dynamic organ
growth at cell-type-specific resolution (Band et al., 2012, 2014).
The ability to map these diverse components onto a common
template using 3DCellAtlas also provides a means to generate
digital single cell databases of plant organ development through
the community deposition of reporter data sets into a common
cellular atlas framework. The generation of compiled single-cell
data would enable meta-analyses to uncover previously undescribed biological relationships at great resolution.
As imaging technologies improve and greater number and diversity of whole-organ 3D data sets are generated, 3DCellAtlas
will enable their discretization, analysis, and integration.
METHODS
Plant Materials and Growth Conditions
Arabidopsis thaliana plants were grown to maturity in environmentally
controlled cabinets using 16 h light (light intensity 150 to 175 mmol m2 s21)
at 22°C and 8 h dark at 18°C. Seeds were harvested, cleaned through
a 500-mm mesh, and stored at 24°C in glassine bags. The line harbored
pRGA:RGA-GUS C-terminal GUS fusion within the Gateway vector
pGWB433 and included 2000 bp of promoter sequence. The promoter
reporter EXPA3:GUS was generated within the pKANGWFS7 Gateway
vector with 2000 bp of promoter sequence. GUS staining was performed
as previously described (Bassel et al., 2014).
Sample Preparation and Image Acquisition
All seeds were surface sterilized using 10% (v/v) Parazone (commercial
bleach) for 5 min and rinsed five times with sterile distilled water. Seeds were
then pipetted onto Petri dishes containing 0.53 Murashige and Skoog basal
salts (Melford) and 0.8% or 1% (w/v) agar (Fisher), pH 5.7. Mature embryo
samples were imbibed for 3 h on agar plates before being placed upon
moist filter paper and dissected using a binocular microscope before being
fixed in 3:1 (v/v) ethanol:acetic acid. Roots at 7 d and hypocotyls at 4 d were
prepared by growing plants on vertical 1% (w/v) agar plates.
For embryos, roots, and hypocotyls, following one night of fixation,
samples were placed in 1% SDS and 0.2 N NaOH overnight and then
washed in distilled water before being enzymatically treated with
a-amylase as previously described (Wuyts et al., 2010). Following amylase treatment, samples were stained with propidium iodide as described
previously (Truernit et al., 2008).
z-Stacks of fixed cleared samples were collected using a 253 oil
objective on a Zeiss LSM 710 confocal microscope at 0.6 Airy units and
a slice interval of 0.4 mm as 16-bit images at 2048 3 2048 resolution.
Identical settings were used for all experiments. GUS signal detection was
collected as previously described (Truernit et al., 2008) with the detector
set to reflectance, gain typically at 400, and offset at 25600. Image
stacks were saved in the Zeiss LSM format, exported into TIFF
stacks using ImageJ, and then imported into MorphoGraphX for
segmentation.
3D Segmentation of Plant Cells
TIFF image stacks of whole plant organs were blurred using the ITK
Gaussian Blur with a radius of 0.5 mm before being segmented using the
ITK Autoseeded Watershed (www.itk.org) at a threshold between 500 and
800. Segmented bitmap stacks were manually corrected for oversegmentation errors within MorphoGraphX by fusing together multiple
labels into the single cells, which were represented using a combination of
the select and paint bucket tools in MorphoGraphX (Kierzkowski et al.,
2012; Roeder et al., 2012). Meshes were generated using the marching
cubes algorithm (Lorensen and Cline, 1987) at a cube spacing of 2 mm and
were not smoothed.
Defining the Intrinsic Coordinate System
A Bezier line containing five control handles was oriented to extend through
the center of the axis of sample roots, hypocotyls, and embryos. A mesh
defining the surface of the organ was generated by first blurring the whole
organ by 5 mm and then generating a two-dimensional curved surface mesh
using the marching cubes surface algorithm at a spacing of 5 mm and
a threshold of 3000 (Kierzkowski et al., 2012). The end of the mesh,
corresponding to the place where the imaging of the organ was limited
due to the objective, was removed within MorphoGraphX using clipping
planes.
With the mesh defining the cells of the organ in Mesh 1, and the surface
mesh in Mesh 2 within MorphoGraphX, the process named “analyze cells
3D” is run. For each triangle in the surface mesh of cell i, a tetrahedron is
formed from the axis origin and the three triangle vertices ðt 1 ; t 2 ; t 3 Þ,
labeled in an anticlockwise manner when viewed from within the cell. The
signed volume of each tetrahedron is calculated, and the sum of these
signed volumes then gives the cell volume,
xvi ¼
∑
tri ðt1 ;t2 ;t3 Þ∈i
t1 $ðt2 3 t3 Þ
:
6
The centroid of the cell is then calculated by summing the midpoints of the
tetrahedral, weighted by their signed volume,
xci ¼
1
xvi
∑
tri ðt1 ;t2 ;t3 Þ∈i
t1 $ðt2 3 t3 Þ t1 þ t2 þ t3
:
4
6
We then calculate the coordinates of each centroid in terms of an intrinsic
coordinate system, using length from the organ tip (longitudinal coordinate
s), radial distance from the Bezier (radial coordinate r), and angle around the
organ (circumferential coordinate u) (Supplemental Figure 2). For each
centroid, the closest point on the Bezier x b ðs i Þ is found, associating each
cell with a position
(longitudinal coordinate) si and a radial
along the Bezier
coordinate r i ¼ x b ðs i Þ 2 x ci . The circumferential coordinate is calculated
by defining a plane
1
tb s 3 r 1 $ m 2 xb s i ¼ 0; ∀ m ∈ R3
that cuts radially through the organ. Here, r 1 is the direction of an arbitrary
cell to the Bezier and t b ðs1 Þ is the tangent to the Bezier at that point. For
each centroid, the closest point on the plane is calculated, which together
Quantitative 3D Cellular Organ Atlases
with the closest point to the Bezier gives two sides of a right-angled
triangle, giving the circumferential angle u i .
Having calculated intrinsic coordinates ðs i ; r i ; u i Þ for each cell i, we
calculate a scaled radial coordinate that maps each centroid to a unique
point of the interior of a cylinder of unit radius, with each successive layer
of different cell types occupying approximately annular shells of different
average radii (Supplemental Figures 1 to 3). This is done by considering all
points on the surface mesh with longitudinal distance s i 2d < s < s i þ d
i
for small, positive d and then selecting the
t on the surface mesh
point x
such that the difference in u coordinate ui 2 uit is minimized. We can
then calculate a scaled radial coordinate rsi ¼ r i =rti , where rti is the
shortest distance from x it to the centerline Bezier.
To inform the radially symmetric portion along the longitudinal distance
of the organ, the first true cortical cell is demarcated, eliminating the
topologically nonradially symmetric columella from the analysis. This
completes the intrinsic description of the organ in terms of the body
coordinates ðs i ; r i ; u i Þ.
Local Cell Axis Alignment
To find the morphology of each cell in terms of intrinsic longitudinal, radial,
and circumferential lengths, we must know the alignment of that cell with
respect to the full organ. Thus, for each cell i it is necessary to find a local
set of “cell” axes. For each cell, we calculate the three local directions
defining its orientation then walk along them to find the cell lengths using
a ray triangle intersect algorithm.
The three local directions are determined in the following way. For
a radially symmetric organ, a local radial axis r b ðs i Þ is given by the
direction from the cell centroid to its nearest point on the Bezier curve.
However, because our organs are not perfectly radially symmetric, we
also calculate the direction to the closest point on the surface mesh
r t ðsi Þ. The cell radial axis r ci is approximated as a combination of the two
directions. Given that cells closer to the outside of the organ will be more
aligned with the surface mesh, and vice versa for cells on the inside of
the organ with the Bezier, we use the scaled radial coordinate r si to
scale the contribution of these two reference axes over the radial
distance of the organ using the linear combination,
r ci ¼ r si r t s i þ 1 2 r si r b s i :
To approximate the cell longitudinal direction lci , we require unit tangent to
the Bezier associated with the cell centroid t b ðsi Þ and the normalized unit
vector t t ðs i Þ of the projection of t b ðs i Þ onto the surface mesh at point x it .
The local cell tangent t ci is then also modeled by a combination of t b ðs i Þ
and t t ðsi Þ linear in the scaled radius r si ,
lci ¼ r si tt s i þ 1 2 r si tb s i ;
so that cells close to the outside match t t ðsi Þ and cells close to the inside match
t b ðsi Þ. This model is the simplest that allows for changes in the organ radius as
a function of longitudinal distance resulting from changing cell alignment and
morphology, as occurs for example toward the root tip (Supplemental Figure 3).
The circumferential axis is then given by the cross-product c ic ¼ l ci 3r ci . To
determine cell lengths along these three oriented local directions Lic ; Rci ; Cci ;
a ray is projected from the centroid in the three local directions (longitudinal,
radial, and circumferential), in both the positive and negative directions. The
distance between the points where the positive and negative rays hit the
perimeter of the cell defines the cell length along this local axis.
Geometry-Based Clustering and Identification of Cell Types
Using the “assign cell types” process, cell types were clustered in a twodimensional heat map plotting a combination of the radial coordinate and
1031
either the circumferential, longitudinal, or radial length. Whichever coordinates are chosen for clustering, the values are scaled by their minimum
and maximum value and spread over the range of the heat map space
(dx ; dy Þ. We generated a two-dimensional function from a sum of Gaussians
centered around the two chosen coordinate values ðdxi ; dyi ) of each cell i. For
a given point on the heat map (x, y), the heat value is calculated as
Gðx; yÞ ¼
∑e
2
2 ðx 2 dxi Þ =2s2 2 y 2 dyi
2
=2s2
;
i
with the smoothing parameter s that can be manually adjusted by the user
within the GUI. Because this smooth function has defined peaks and valleys,
demarcation of cell types along these maxima and minima is possible and
performed through the GUI implemented within MorphoGraphX.
Using the “assign columella” process, cells beyond the radially symmetric portion of the organ are labeled as columella. To determine which
columella cells form part of the root cap, the ratio of the total surface area of
each columella cell to the sum of the areas interacting with its neighboring
cells is calculated. Those cells that are root cap lie on the outside of the
organ, and as such there is a significant deficit (at least 20%) in these areas
compared with interior cells. This measure taken together with scaled radius
is used to differentiate the root cap cells lying over the columella.
Cell identity labels are stored as “parents” within MorphoGraphX. The
geometric properties of cells within a given sample can be visualized in
situ within MorphoGraphX using the “display cell data” process. A dropdown menu allows for the different geometric features to be visualized
individually.
Identification of Mislabeled Cells Using Graph Topology
Triangular meshes containing the 3D cell geometry and arrangement of
cells within plant organs were used to determine and quantify the
interfaces between cells as previously described (Yoshida et al., 2014).
Triangles that were shared between adjacent cells were identified and
defined edges within the connectivity network. Using the cellular connectivity network data and cellular annotation of the labels, the pairwise
relationships between cells can be examined (Long et al., 2009). In this
way, cells that have been incorrectly annotated can be identified based on
the impossibility of their association with their neighbors. An association
between the vasculature and cortex indicates the absence or mislabeling
of an endodermal cell and is the most common misannotation error
(Table 1). Using the “topological check” feature, cells or nodes possessing
a user-defined threshold of impossible edges are identified by examining
all connections and comparing them with lists of possible connections.
Nodes with a greater number of impossible edges than the threshold are
identified as misannotated.
Cells that have been mislabeled as vasculature are relabeled as endodermis or air spaces depending on their circumferential lengths based on
the length value of previously accurately labeled cells. All other potentially
misannotated cell labels can be highlighted within MorphoGraphX where
they are visually inspected and reassigned to their proper annotation.
Cortical Cell Referencing System
Following the identification of cell types using the “assign cell types”
process, the cortical cell referencing system was set up using “assign
cortical cells.” Here, the cortical cell in a user-defined position is selected,
the tangential lengths of every cortical cell are taken as a function of the
cells’ longitudinal coordinates, and the data are fitted to a third order
polynomial via a least-squares method. This creates an average “fitted
length” of cortical cells as a function of their longitudinal coordinate, which
is used to divide the cells into bins. Each cell is then associated with this
intrinsic cortical numbering system via nearest-neighbor interpolation on
its longitudinal coordinate.
1032
The Plant Cell
Statistical Analysis of Growth Data
The process “3D cell growth analysis” was used to analyze the cellular
geometric data generated by 3DCellAtlas. Data from individual samples were
saved and placed within a single directory where they were analyzed. Ratios of
means for cell size metrics (r) were measured by reading data calculated in
MorphoGraphX from multiple stacks and grouping the data by cell type, and
then associated cortical cell; from these vectors, the r was calculated using
r ¼ eT 2 C ;
where T and C are arrays containing the natural logarithms of the treatment
are their corresponding sample
and control data, respectively, and T and C
mean values. Approximate confidence intervals were calculated as
pffiffiffiffi
pffiffiffiffi
nT þS2C = nc Þ
lower CI ¼ eðT 2 CÞ 2 tðST =
2
pffiffiffiffi
ðT 2 C Þþ tðS2T =pffiffiffiffi
nT þS2C = nc Þ
upper CI ¼ e
;
where t is the critical value of the t-distribution at the (1 2 aÞ% percentile point
with nT þ nC 2 2 degrees of freedom. Here, a was set to 0.05 to produce 95%
confidence intervals. S2T and S2C are the variances of the natural logarithm data
sets, and nT and nC are the sample sizes of the T and C data sets.
E-factors were defined for the differences in radial DR ¼ Rend 2 Rbeg ,
longitudinal DL ¼ Lend 2 Lbeg , and circumferential DC ¼ Cend 2 Cbeg
cell lengths measured at the beginning and the end of the experiment
using the same vectors as previously, with the following:
eCircumferential ¼
eRadial ¼
DC
ðDC þ DR þ DLÞ
DR
ðDC þ DR þ DLÞ
eLongitudinal ¼
DL
:
ðDC þ DR þ DLÞ
Data presented were smoothed using a sliding window average of three
associated cortical cell positions, one cell on either side. Cells in the first
and last position within samples were reduced to the average of two cells,
that being their single neighbor.
window. Text files containing the mean and confidence interval for calculated reporter data are exported from the process for further use.
The statistical relationships between reporters and cell volume were
established by performing linear regression after log-transforming data on
each cell type separately, with these comparisons being performed on the
same imaged materials. The relationship between RGA and EXPA3 measurements was collected from different experimental materials so it was not
possible to assess this relationship directly. Here, RGA and EXPA3 data
were averaged at each cell type and associated cortical cell level, and
a linear model was fit to examine their relationship for each cell type.
Accession Numbers
Sequence data from this article can be found in the GenBank/EMBL libraries
under accession numbers: At2g37640 (EXPA3) and At2g01570 (RGA).
Supplemental Data
Supplemental Figure 1. 3D segmentations of an Arabidopsis root,
hypocotyl, and embryo.
Supplemental Figure 2. Cylindrical coordinate system used to orient
radially symmetric plant organs.
Supplemental Figure 3. Calculation of scaled radial distance.
Supplemental Figure 4. Use of scaled radial distance to adjust for
geometric irregularities.
Supplemental Figure 5. Heat maps and cell-type cluster selection.
Supplemental Figure 6. The conversion of shared triangles in meshes
between adjacent cells and a cellular connectivity network.
Supplemental Figure 7. Plots showing hypocotyl 3D anisotropy as
a function of associated cortical cell in a light-grown Arabidopsis
hypocotyl 4 d after germination.
Supplemental Figure 8. Plots showing hypocotyl 3D anisotropy as
a function of associated cortical cell in an ACC-treated light-grown
Arabidopsis hypocotyl 4 d after germination.
Supplemental Movie 1. Demonstration of cell type identification and
topological correction on an Arabidopsis embryonic hypocotyl using
3DCellAtlas.
ACKNOWLEDGMENTS
Statistical Analysis of Reporter Data
Reporter concentration within the context of the 3D segmented organ
mesh was determined by taking the signal average interior signal value
from the MorphoGraphX heat map function. These data were saved as
CSV files from within this process and analyzed using 3DCellAtlas with the
“reporter abundance analysis 3D” process. The reporter concentration
values for each cell were assigned to the appropriate cell type and position (associated cortical cell) by matching these data with corresponding
files generated from cell type and indexing in previous processes described above. Data from multiple samples were pooled together into bins
representing unique cells.
The statistical analysis of reporter cellular concentrations calculated
the mean reporter concentration for each cell type and associated cortical
cell as vectors, as described above for cell geometry data. The 95%
confidence intervals were computed using bootstrapping, with a minimum sample size of seven cells.
These data were projected onto heat maps for visualization in
MorphoGraphX by joining the computed metrics by cell type and associated cortical cell to a selected 3D segmented organ present in the active
G.W.B. and A.T.T. were supported by BBSRC Grant BB/L010232/1, and
P.S. and A.T.A.W. were supported by BBSRC Grant BB/J017604/1.
T.D.M.J. is supported by a Royal Commission for the Exhibition of 1851
Research Fellowship. The work of M.T. on this project was supported by
EPSRC Grant EP/K022547/1. R.S.S. was supported by the Swiss National
Science Foundation interdisciplinary project grant CR32I3_143833. We thank
Noni Franklin-Tong and Carlos Flores Ortiz for sharing Papaver rhoeas seeds.
AUTHOR CONTRIBUTIONS
T.D.M.J. developed the software and analyzed data. P.S. performed the
research and analyzed data. S.S. and R.S.S. implemented the software
within MorphoGraphX. A.T.T., M.T., and A.T.A.W. developed the statistical analyses. G.W.B. designed the research, performed the research,
analyzed data, and wrote the article with input from all authors.
Received February 24, 2015; revised March 24, 2015; accepted March
27, 2015; published April 21, 2015.
Quantitative 3D Cellular Organ Atlases
REFERENCES
Band, L.R., Úbeda-Tomás, S., Dyson, R.J., Middleton, A.M., Hodgman,
T.C., Owen, M.R., Jensen, O.E., Bennett, M.J., and King, J.R. (2012).
Growth-induced hormone dilution can explain the dynamics of plant
root cell elongation. Proc. Natl. Acad. Sci. USA 109: 7577–7582.
Band, L.R., et al. (2014). Systems analysis of auxin transport in the
Arabidopsis root apex. Plant Cell 26: 862–875.
Bassel, G.W., Gaudinier, A., Brady, S.M., Hennig, L., Rhee, S.Y., and De
Smet, I. (2012). Systems analysis of plant functional, transcriptional,
physical interaction, and metabolic networks. Plant Cell 24: 3859–3875.
Bassel, G.W., Stamm, P., Mosca, G., Barbier de Reuille, P., Gibbs,
D.J., Winter, R., Janka, A., Holdsworth, M.J., and Smith, R.S.
(2014). Mechanical constraints imposed by 3D cellular geometry
and arrangement modulate growth patterns in the Arabidopsis embryo.
Proc. Natl. Acad. Sci. USA 111: 8685–8690.
Brady, S.M., Orlando, D.A., Lee, J.Y., Wang, J.Y., Koch, J.,
Dinneny, J.R., Mace, D., Ohler, U., and Benfey, P.N. (2007). A
high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318: 801–806.
Braun, P., et al.; Arabidopsis Interactome Mapping Consortium
(2011) Evidence for network evolution in an Arabidopsis interactome map. Science 333: 601–607.
Busch, W., Moore, B.T., Martsberger, B., Mace, D.L., Twigg, R.W.,
Jung, J., Pruteanu-Malinici, I., Kennedy, S.J., Fricke, G.K., Clark,
R.L., Ohler, U., and Benfey, P.N. (2012). A microfluidic device and
computational platform for high-throughput live imaging of gene expression. Nat. Methods 9: 1101–1106.
Cao, D., Cheng, H., Wu, W., Soo, H.M., and Peng, J. (2006). Gibberellin mobilizes distinct DELLA-dependent transcriptomes to
regulate seed germination and floral development in Arabidopsis.
Plant Physiol. 142: 509–525.
Cloetens, P., Mache, R., Schlenker, M., and Lerbs-Mache, S. (2006).
Quantitative phase tomography of Arabidopsis seeds reveals intercellular void network. Proc. Natl. Acad. Sci. USA 103: 14626–
14630.
Cosgrove, D.J. (2005). Growth of the plant cell wall. Nat. Rev. Mol.
Cell Biol. 6: 850–861.
Federici, F., Dupuy, L., Laplaze, L., Heisler, M., and Haseloff, J.
(2012). Integrated genetic and computation methods for in planta
cytometry. Nat. Methods 9: 483–485.
Fernandez, R., Das, P., Mirabet, V., Moscardi, E., Traas, J., Verdeil,
J.L., Malandain, G., and Godin, C. (2010). Imaging plant growth in
4D: robust tissue reconstruction and lineaging at cell resolution.
Nat. Methods 7: 547–553.
French, A.P., Wilson, M.H., Kenobi, K., Dietrich, D., Voß, U.,
Ubeda-Tomás, S., Pridmore, T.P., and Wells, D.M. (2012). Identifying biological landmarks using a novel cell measuring image
analysis tool: Cell-o-Tape. Plant Methods 8: 7.
Gendreau, E., Traas, J., Desnos, T., Grandjean, O., Caboche, M.,
and Höfte, H. (1997). Cellular basis of hypocotyl growth in Arabidopsis thaliana. Plant Physiol. 114: 295–305.
Goda, H., et al. (2008). The AtGenExpress hormone and chemical
treatment data set: experimental design, data evaluation, model
data analysis and data access. Plant J. 55: 526–542.
Harberd, N.P., Belfield, E., and Yasumura, Y. (2009). The angiosperm gibberellin-GID1-DELLA growth regulatory mechanism: how
an “inhibitor of an inhibitor” enables flexible response to fluctuating
environments. Plant Cell 21: 1328–1339.
Hejnowicz, Z. (1984). Trajectories of principal directions of growth,
natural coordinate system in growing-plant organ. Acta Soc. Bot.
Pol. Pol. Tow. Bot. 53: 29–42.
Kierzkowski, D., Nakayama, N., Routier-Kierzkowska, A.L., Weber,
A., Bayer, E., Schorderet, M., Reinhardt, D., Kuhlemeier, C., and
1033
Smith, R.S. (2012). Elastic domains regulate growth and organogenesis in the plant shoot apical meristem. Science 335: 1096–1099.
Liao, C.Y., Smet, W., Brunoud, G., Yoshida, S., Vernoux, T., and
Weijers, D. (2015). Reporters for sensitive and quantitative measurement of auxin response. Nat. Methods 12: 207–210.
Long, F., Peng, H., Liu, X., Kim, S.K., and Myers, E. (2009). A 3D
digital atlas of C. elegans and its application to single-cell analyses.
Nat. Methods 6: 667–672.
Long, F.H., Peng, H., Liu, X., Kim, S., and Myers, G. (2008). Automatic
recognition of cells (ARC) for 3D images of C-elegans. In Lecture Notes
in Computer Science: Research in Computational Molecular Biology,
Vol. 4955, M. Vingron and L. Wong, eds (Springer), pp. 128–139.
Lorensen, W.E., and Cline, H.E. (1987). Marching cubes: A high
resolution 3D surface construction algorithm. In SIGGRAPH ’87
Proceedings of the 14th Annual Conference on Computer Graphics
and Interactive Techniques, pp. 163–169.
Maizel, A., von Wangenheim, D., Federici, F., Haseloff, J., and
Stelzer, E.H. (2011). High-resolution live imaging of plant growth in
near physiological bright conditions using light sheet fluorescence
microscopy. Plant J. 68: 377–385.
Moreno, N., Bougourd, S., Haseloff, J., and Feijo, J. (2006). Imaging
Plant Cells. (New York: SpringerScience and Business Media).
Pound, M.P., French, A.P., Wells, D.M., Bennett, M.J., and
Pridmore, T.P. (2012). CellSeT: novel software to extract and analyze structured networks of plant cells from confocal images. Plant
Cell 24: 1353–1361.
Rhee, S.Y., and Mutwil, M. (2014). Towards revealing the functions of
all genes in plants. Trends Plant Sci. 19: 212–221.
Roeder, A.H.K., Cunha, A., Burl, M.C., and Meyerowitz, E.M. (2012).
A computational image analysis glossary for biologists. Development
139: 3071–3080.
Schauer, N., and Fernie, A.R. (2006). Plant metabolomics: towards
biological function and mechanism. Trends Plant Sci. 11: 508–516.
Schmidt, T., Pasternak, T., Liu, K., Blein, T., Aubry-Hivet, D.,
Dovzhenko, A., Duerr, J., Teale, W., Ditengou, F.A., Burkhardt,
H., Ronneberger, O., and Palme, K. (2014). The iRoCS Toolbox—
3D analysis of the plant root apical meristem at cellular resolution.
Plant J. 77: 806–814.
Sena, G., Frentz, Z., Birnbaum, K.D., and Leibler, S. (2011). Quantitation of cellular dynamics in growing Arabidopsis roots with light
sheet microscopy. PLoS ONE 6: e21303.
Smalle, J., Haegman, M., Kurepa, J., Van Montagu, M., and Straeten,
D.V. (1997). Ethylene can stimulate Arabidopsis hypocotyl elongation in
the light. Proc. Natl. Acad. Sci. USA 94: 2756–2761.
Truernit, E., Bauby, H., Dubreucq, B., Grandjean, O., Runions, J.,
Barthélémy, J., and Palauqui, J.C. (2008). High-resolution wholemount imaging of three-dimensional tissue organization and gene
expression enables the study of phloem development and structure
in Arabidopsis. Plant Cell 20: 1494–1503.
Ubeda-Tomás, S., Swarup, R., Coates, J., Swarup, K., Laplaze, L.,
Beemster, G.T.S., Hedden, P., Bhalerao, R., and Bennett, M.J.
(2008). Root growth in Arabidopsis requires gibberellin/DELLA signalling in the endodermis. Nat. Cell Biol. 10: 625–628.
Wuyts, N., Palauqui, J.C., Conejero, G., Verdeil, J.L., Granier, C.,
and Massonnet, C. (2010). High-contrast three-dimensional imaging of the Arabidopsis leaf enables the analysis of cell dimensions
in the epidermis and mesophyll. Plant Methods 6: 17.
Yoshida, S., Mandel, T., and Kuhlemeier, C. (2011). Stem cell activation
by light guides plant organogenesis. Genes Dev. 25: 1439–1450.
Yoshida, S., Barbier de Reuille, P., Lane, B., Bassel, G.W.,
Prusinkiewicz, P., Smith, R.S., and Weijers, D. (2014). Genetic
control of plant development by overriding a geometric division rule.
Dev. Cell 29: 75–87.
Supplemental Data. Montenegro-Johnson et al. Plant Cell (2015) 10.1105/tpc.15.00175
SUPPLEMENTAL FIGURES
Supplemental Figure 1. 3D segmentations of an Arabidopsis (A-C) root, (D-E) hypocotyl
and (G-I) embryo. Images show the surface (A, D, G), end view (B, E, H) and longitudinal
section (C, F, I) of each organ. Different colors indicate the identification of unique
segments. White scale bars indicate 50 m.
Supplemental Data. Montenegro-Johnson et al. Plant Cell (2015) 10.1105/tpc.15.00175
Supplemental Figure 2. Cylindrical co-ordinate system used to orient radially symmetric
plant organs. (A) Cylinder representing the radially symmetric organ with longitudinal axis
s, radial length r and circumferential axis . (B) Plot of cell centroids of the cells within a
hypocotyl with epidermal cells colored cyan, outer cortical cells pink, inner cortical cells
yellow and endodermal cells black.
Supplemental Data. Montenegro-Johnson et al. Plant Cell (2015) 10.1105/tpc.15.00175
Supplemental Figure 3. Calculation of scaled radial distance. (A) Cross section of an
embryonic hypocotyl showing the asymmetry shape and distribution of points across the
axis. (B) Cross section of the axis after the radial distances have been scaled.
Supplemental Data. Montenegro-Johnson et al. Plant Cell (2015) 10.1105/tpc.15.00175
Supplemental Figure 4. Use of scaled radial distance to adjust for geometric
irregularities. (A) Adjustment for radial tapering in the longitudinal direction. See Methods.
(B) Confocal image section of an Arabidopsis embryo axis showing native tapering
geometry towards the radicle tip. (C) Adjustment in the cell radial axis direction to account
for cross-sectional squeezing. See Methods. (D) Cross section of an embryonic hypocotyl
indicating cross-sectional squeezing.
Supplemental Data. Montenegro-Johnson et al. Plant Cell (2015) 10.1105/tpc.15.00175
Supplemental Figure 5. Heatmaps and cell type cluster selection. (A-C) 7 day old light
grown hypocotyl. (A) Cell clusters scaled by radial distance on the x-axis and azimuthal
length on the y-axis. (B) Locations of the distinct cell clusters and GUI-based click
locations indicated by clicks within the GUI. (C) Same as (B) with the locations and parent
labels associated with each click highlighted for visualization. Labels in yellow represent
true cell labels and those in red air spaces and missegmented cells. (D-I) A mature
Arabidopsis root separated into proximal (D-F) and distal (G-I) regions. (D) Heat map of
cells from the QC to the root cap split scaled the same as (A). (E) The locations of the
clicks to identify distinct cell clusters. (F) Highlighted locations of cell cluster click locations
using the same colors as (C). (J-O) A mature Arabidopsis embryo separated into the
radicle (J-L) and hypocotyl (M-O) regions. (J) Heatmap of cell clusters in the embryonic
hypocotyl. (K) Click locations within the GUI to identify cell clusters. (L) Same as (K) with
click locations highlighted and using the same color scheme as (C). (M) Clusters of the
cells of the radicle in the mature embryo. (N) Location of the clicks to identify cell positions.
(O) Same as (N) with the location of clicks highlighted using the color scheme in (C).
Supplemental Data. Montenegro-Johnson et al. Plant Cell (2015) 10.1105/tpc.15.00175
Supplemental Figure 6. The conversion of shared triangles in meshes between
adjacent cells and a cellular connectivity network. (A) Mesh and surface view of 5
cortical cells from an embryonic hypocotyl. (B) Conversion of the cells in (A) into a
graph representing cellular connectivity. Edge line width represents relative shared
surface areas between adjacent cells.
Supplemental Data. Montenegro-Johnson et al. Plant Cell (2015) 10.1105/tpc.15.00175
Supplemental Figure 7. Plots showing hypocotyl 3D anisotropy as a function of
associated cortical cell in a light-grown Arabidopsis hypocotyl 4 d after germination.
(A-D) Change in volume ratio by cell type.
(E-H) Change in area ratio by cell type.
(I-L) Change in longitudinal length ratio by cell type.
(M-P) Change in radial length ratio by cell type.
(Q-T) Change in circumferential length ratio by cell type.
The associated cortical cell position is on the x-axis and the ratio of the treatment mean
over the control mean on the y-axis. The mean is plotted in black (solid line) and the
95% confidence interval indicated by green lines. The smoothed data line, across a
window of 3 cells, is indicated in red (dotted line).
Supplemental Data. Montenegro-Johnson et al. Plant Cell (2015) 10.1105/tpc.15.00175
Supplemental Figure 8. Plots showing hypocotyl 3D anisotropy as a function of
associated cortical cell in an ACC-treated light-grown Arabidopsis hypocotyl 4 d after
germination.
(A-D) Change in volume ratio by cell type.
(E-H) Change in area ratio by cell type.
(I-L) Change in longitudinal length ratio by cell type.
(M-P) Change in radial length ratio by cell type.
(Q-T) Change in circumferential length ratio by cell type.
The associated cortical cell position is on the x-axis and the ratio of the treatment mean
over the control mean on the y-axis. The mean is plotted in black (solid line) and the
95% confidence interval indicated by green lines. The smoothed data line, across a
window of 3 cells, is indicated in red (dotted line).
Supplemental Data. Montenegro-Johnson et al. Plant Cell (2015) 10.1105/tpc.15.00175
Digital Single-Cell Analysis of Plant Organ Development Using 3DCellAtlas
Thomas D. Montenegro-Johnson, Petra Stamm, Soeren Strauss, Alexander T. Topham, Michail Tsagris,
Andrew T.A. Wood, Richard S. Smith and George W. Bassel
Plant Cell 2015;27;1018-1033; originally published online April 21, 2015;
DOI 10.1105/tpc.15.00175
This information is current as of June 26, 2015
Supplemental Data
http://www.plantcell.org/content/suppl/2015/04/08/tpc.15.00175.DC1.html
References
This article cites 33 articles, 16 of which can be accessed free at:
http://www.plantcell.org/content/27/4/1018.full.html#ref-list-1
Permissions
https://www.copyright.com/ccc/openurl.do?sid=pd_hw1532298X&issn=1532298X&WT.mc_id=pd_hw1532298X
eTOCs
Sign up for eTOCs at:
http://www.plantcell.org/cgi/alerts/ctmain
CiteTrack Alerts
Sign up for CiteTrack Alerts at:
http://www.plantcell.org/cgi/alerts/ctmain
Subscription Information
Subscription Information for The Plant Cell and Plant Physiology is available at:
http://www.aspb.org/publications/subscriptions.cfm
© American Society of Plant Biologists
ADVANCING THE SCIENCE OF PLANT BIOLOGY